@article{AlchikhConradObermeieretal.2024, author = {Alchikh, Maren and Conrad, Tim and Obermeier, Patrick and Ma, Xiaolin and Schweiger, Brunhilde and Opota, Onya and Rath, Barbara}, title = {Disease Burden and Inpatient Management of Children with Acute Respiratory Viral Infections during the Pre-COVID Era in Germany: A Cost-of-Illness Study}, volume = {16}, journal = {Viruses}, number = {4}, doi = {10.3390/v16040507}, year = {2024}, abstract = {Respiratory viral infections (RVIs) are common reasons for healthcare consultations. The inpatient management of RVIs consumes significant resources. From 2009 to 2014, we assessed the costs of RVI management in 4776 hospitalized children aged 0-18 years participating in a quality improvement program, where all ILI patients underwent virologic testing at the National Reference Centre followed by detailed recording of their clinical course. The direct (medical or non-medical) and indirect costs of inpatient management outside the ICU ('non-ICU') versus management requiring ICU care ('ICU') added up to EUR 2767.14 (non-ICU) vs. EUR 29,941.71 (ICU) for influenza, EUR 2713.14 (non-ICU) vs. EUR 16,951.06 (ICU) for RSV infections, and EUR 2767.33 (non-ICU) vs. EUR 14,394.02 (ICU) for human rhinovirus (hRV) infections, respectively. Non-ICU inpatient costs were similar for all eight RVIs studied: influenza, RSV, hRV, adenovirus (hAdV), metapneumovirus (hMPV), parainfluenza virus (hPIV), bocavirus (hBoV), and seasonal coronavirus (hCoV) infections. ICU costs for influenza, however, exceeded all other RVIs. At the time of the study, influenza was the only RVI with antiviral treatment options available for children, but only 9.8\% of influenza patients (non-ICU) and 1.5\% of ICU patients with influenza received antivirals; only 2.9\% were vaccinated. Future studies should investigate the economic impact of treatment and prevention of influenza, COVID-19, and RSV post vaccine introduction.}, language = {en} } @article{BetzHammerschmidtZschiedrichetal.2024, author = {Betz, Fridtjof and Hammerschmidt, Martin and Zschiedrich, Lin and Burger, Sven and Binkowski, Felix}, title = {Efficient rational approximation of optical response functions with the AAA algorithm}, volume = {18}, journal = {Laser Photonics Rev.}, arxiv = {http://arxiv.org/abs/2403.19404}, doi = {10.1002/lpor.202400584}, pages = {2400584}, year = {2024}, language = {en} } @article{SherrattSrivastavaAinslieetal.2024, author = {Sherratt, Katharine and Srivastava, Ajitesh and Ainslie, Kylie and Singh, David E. and Cublier, Aymar and Marinescu, Maria Cristina and Carretero, Jesus and Garcia, Alberto Cascajo and Franco, Nicolas and Willem, Lander and Abrams, Steven and Faes, Christel and Beutels, Philippe and Hens, Niel and M{\"u}ller, Sebastian and Charlton, Billy and Ewert, Ricardo and Paltra, Sydney and Rakow, Christian and Rehmann, Jakob and Conrad, Tim and Sch{\"u}tte, Christof and Nagel, Kai and Abbott, Sam and Grah, Rok and Niehus, Rene and Prasse, Bastian and Sandmann, Frank and Funk, Sebastian}, title = {Characterising information gains and losses when collecting multiple epidemic model outputs}, volume = {47}, journal = {Epidemics}, publisher = {Elsevier BV}, issn = {1755-4365}, doi = {10.1016/j.epidem.2024.100765}, year = {2024}, abstract = {Collaborative comparisons and combinations of epidemic models are used as policy-relevant evidence during epidemic outbreaks. In the process of collecting multiple model projections, such collaborations may gain or lose relevant information. Typically, modellers contribute a probabilistic summary at each time-step. We compared this to directly collecting simulated trajectories. We aimed to explore information on key epidemic quantities; ensemble uncertainty; and performance against data, investigating potential to continuously gain information from a single cross-sectional collection of model results. Methods We compared July 2022 projections from the European COVID-19 Scenario Modelling Hub. Five modelling teams projected incidence in Belgium, the Netherlands, and Spain. We compared projections by incidence, peaks, and cumulative totals. We created a probabilistic ensemble drawn from all trajectories, and compared to ensembles from a median across each model's quantiles, or a linear opinion pool. We measured the predictive accuracy of individual trajectories against observations, using this in a weighted ensemble. We repeated this sequentially against increasing weeks of observed data. We evaluated these ensembles to reflect performance with varying observed data. Results. By collecting modelled trajectories, we showed policy-relevant epidemic characteristics. Trajectories contained a right-skewed distribution well represented by an ensemble of trajectories or a linear opinion pool, but not models' quantile intervals. Ensembles weighted by performance typically retained the range of plausible incidence over time, and in some cases narrowed this by excluding some epidemic shapes. Conclusions. We observed several information gains from collecting modelled trajectories rather than quantile distributions, including potential for continuously updated information from a single model collection. The value of information gains and losses may vary with each collaborative effort's aims, depending on the needs of projection users. Understanding the differing information potential of methods to collect model projections can support the accuracy, sustainability, and communication of collaborative infectious disease modelling efforts. Data availability All code and data available on Github: https://github.com/covid19-forecast-hub-europe/aggregation-info-loss}, language = {en} } @misc{BetzHammerschmidtZschiedrichetal.2024, author = {Betz, Fridtjof and Hammerschmidt, Martin and Zschiedrich, Lin and Burger, Sven and Binkowski, Felix}, title = {Source code and simulation results: Efficient rational approximation of optical response functions with the AAA algorithm}, journal = {Zenodo}, doi = {10.5281/zenodo.10853692}, pages = {doi: 10.5281/zenodo.10853692}, year = {2024}, language = {en} } @inproceedings{MexiShamsiBesanconetal.2024, author = {Mexi, Gioni and Shamsi, Somayeh and Besan{\c{c}}on, Mathieu and Bodic, Pierre}, title = {Probabilistic Lookahead Strong Branching via a Stochastic Abstract Branching Model}, volume = {14743}, booktitle = {Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2024}, doi = {10.1007/978-3-031-60599-4_4}, year = {2024}, abstract = {Strong Branching (SB) is a cornerstone of all modern branching rules used in the Branch-and-Bound (BnB) algorithm, which is at the center of Mixed-Integer Programming solvers. In its full form, SB evaluates all variables to branch on and then selects the one producing the best relaxation, leading to small trees, but high runtimes. State-of-the-art branching rules therefore use SB with working limits to achieve both small enough trees and short run times. So far, these working limits have been established empirically. In this paper, we introduce a theoretical approach to guide how much SB to use at each node within the BnB. We first define an abstract stochastic tree model of the BnB algorithm where the geometric mean dual gains of all variables follow a given probability distribution. This model allows us to relate expected dual gains to tree sizes and explicitly compare the cost of sampling an additional SB candidate with the reward in expected tree size reduction. We then leverage the insight from the abstract model to design a new stopping criterion for SB, which fits a distribution to the dual gains and, at each node, dynamically continues or interrupts SB. This algorithm, which we refer to as Probabilistic Lookahead Strong Branching, improves both the tree size and runtime over MIPLIB instances, providing evidence that the method not only changes the amount of SB, but allocates it better.}, language = {en} } @article{BetzBinkowskiKuenetal.2024, author = {Betz, Fridtjof and Binkowski, Felix and Kuen, Lilli and Burger, Sven}, title = {Version 2 — RPExpand: Software for Riesz projection expansion of resonance phenomena}, volume = {26}, journal = {SoftwareX}, doi = {10.1016/j.softx.2024.101694}, pages = {101694}, year = {2024}, language = {en} } @article{PlockBinkowskiZschiedrichetal.2024, author = {Plock, Matthias and Binkowski, Felix and Zschiedrich, Lin and Schneider, Phillip-Immanuel and Burger, Sven}, title = {Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes}, volume = {41}, journal = {J. Opt. Soc. Am. B}, arxiv = {http://arxiv.org/abs/2402.09420}, doi = {10.1364/JOSAB.505767}, pages = {850}, year = {2024}, language = {en} } @misc{PlockBinkowskiZschiedrichetal.2024, author = {Plock, Matthias and Binkowski, Felix and Zschiedrich, Lin and Schneider, Phillip-Immanuel and Burger, Sven}, title = {Research data for "Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes"}, journal = {Zenodo}, doi = {10.5281/zenodo.8131611}, pages = {doi: 10.5281/zenodo.8131611}, year = {2024}, language = {en} } @misc{MusialSekSchneideretal.2024, author = {Musial, Anna and Sek, Grzegorz and Schneider, Philipp-Immanuel and Reitzenstein, Stephan}, title = {Fiber-coupled solid-state-based single-photon sources}, journal = {Specialty Optical Fibers}, publisher = {Woodhead Publishing}, doi = {10.1016/B978-0-443-18495-6.00004-4}, pages = {345 -- 384}, year = {2024}, language = {de} } @article{EiflerGleixner2024, author = {Eifler, Leon and Gleixner, Ambros}, title = {Safe and verified Gomory mixed integer cuts in a rational MIP framework}, volume = {34}, journal = {SIAM Journal on Optimization}, number = {1}, doi = {10.1137/23M156046X}, year = {2024}, abstract = {This paper is concerned with the exact solution of mixed-integer programs (MIPs) over the rational numbers, i.e., without any roundoff errors and error tolerances. Here, one computational bottleneck that should be avoided whenever possible is to employ large-scale symbolic computations. Instead it is often possible to use safe directed rounding methods, e.g., to generate provably correct dual bounds. In this work, we continue to leverage this paradigm and extend an exact branch-and-bound framework by separation routines for safe cutting planes, based on the approach first introduced by Cook, Dash, Fukasawa, and Goycoolea in 2009 [INFORMS J. Comput., 21 (2009), pp. 641-649]. Constraints are aggregated safely using approximate dual multipliers from an LP solve, followed by mixed-integer rounding to generate provably valid, although slightly weaker inequalities. We generalize this approach to problem data that is not representable in floating-point arithmetic, add routines for controlling the encoding length of the resulting cutting planes, and show how these cutting planes can be verified according to the VIPR certificate standard. Furthermore, we analyze the performance impact of these cutting planes in the context of an exact MIP framework, showing that we can solve 21.5\% more instances to exact optimality and reduce solving times by 26.8\% on the MIPLIB 2017 benchmark test set.}, language = {en} } @inproceedings{GhannamGleixner2023, author = {Ghannam, Mohammed and Gleixner, Ambros}, title = {Hybrid genetic search for dynamic vehicle routing with time windows}, booktitle = {Proceedings of Conference of the Society for Operations Research in Germany}, year = {2023}, language = {en} } @inproceedings{HoenOertelGleixneretal.2024, author = {Hoen, Alexander and Oertel, Andy and Gleixner, Ambros and Nordstr{\"o}m, Jakob}, title = {Certifying MIP-based presolve reductions for 0-1 integer linear programs}, volume = {14742}, booktitle = {Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2024}, doi = {10.1007/978-3-031-60597-0_20}, year = {2024}, abstract = {It is well known that reformulating the original problem can be crucial for the performance of mixed-integer programming (MIP) solvers. To ensure correctness, all transformations must preserve the feasibility status and optimal value of the problem, but there is currently no established methodology to express and verify the equivalence of two mixed-integer programs. In this work, we take a first step in this direction by showing how the correctness of MIP presolve reductions on - integer linear programs can be certified by using (and suitably extending) the VeriPB tool for pseudo-Boolean proof logging. Our experimental evaluation on both decision and optimization instances demonstrates the computational viability of the approach and leads to suggestions for future revisions of the proof format that will help to reduce the verbosity of the certificates and to accelerate the certification and verification process further.}, language = {en} } @article{KuenLoefflerTsarapkinetal.2024, author = {Kuen, Lilli and L{\"o}ffler, Lorenz and Tsarapkin, Aleksei and Zschiedrich, Lin and Feichtner, Thorsten and Burger, Sven and H{\"o}flich, Katja}, title = {Chiral and directional optical emission from a dipole source coupled to a helical plasmonic antenna}, volume = {124}, journal = {Appl. Phys. Lett.}, arxiv = {http://arxiv.org/abs/2402.00664}, doi = {10.1063/5.0201748}, pages = {231102}, year = {2024}, language = {en} } @article{AmiranashviliLuedkeLietal.2024, author = {Amiranashvili, Tamaz and L{\"u}dke, David and Li, Hongwei Bran and Zachow, Stefan and Menze, Bjoern}, title = {Learning continuous shape priors from sparse data with neural implicit functions}, volume = {94}, journal = {Medical Image Analysis}, doi = {10.1016/j.media.2024.103099}, pages = {103099}, year = {2024}, abstract = {Statistical shape models are an essential tool for various tasks in medical image analysis, including shape generation, reconstruction and classification. Shape models are learned from a population of example shapes, which are typically obtained through segmentation of volumetric medical images. In clinical practice, highly anisotropic volumetric scans with large slice distances are prevalent, e.g., to reduce radiation exposure in CT or image acquisition time in MR imaging. For existing shape modeling approaches, the resolution of the emerging model is limited to the resolution of the training shapes. Therefore, any missing information between slices prohibits existing methods from learning a high-resolution shape prior. We propose a novel shape modeling approach that can be trained on sparse, binary segmentation masks with large slice distances. This is achieved through employing continuous shape representations based on neural implicit functions. After training, our model can reconstruct shapes from various sparse inputs at high target resolutions beyond the resolution of individual training examples. We successfully reconstruct high-resolution shapes from as few as three orthogonal slices. Furthermore, our shape model allows us to embed various sparse segmentation masks into a common, low-dimensional latent space — independent of the acquisition direction, resolution, spacing, and field of view. We show that the emerging latent representation discriminates between healthy and pathological shapes, even when provided with sparse segmentation masks. Lastly, we qualitatively demonstrate that the emerging latent space is smooth and captures characteristic modes of shape variation. We evaluate our shape model on two anatomical structures: the lumbar vertebra and the distal femur, both from publicly available datasets.}, language = {en} } @misc{BetzBinkowskiBurgeretal.2023, author = {Betz, Fridtjof and Binkowski, Felix and Burger, Sven and Kuen, Lilli}, title = {RPExpand (Version 2.0)}, journal = {Zenodo}, doi = {10.5281/zenodo.10371002}, pages = {doi: 10.5281/zenodo.10371002}, year = {2023}, language = {en} } @misc{Peter2023, type = {Master Thesis}, author = {Peter, Clea}, title = {Improving the Realism of Synthetic Cryogenic Electron Micrographs Using Generative Adversarial Networks}, year = {2023}, abstract = {This thesis addresses the problem of synthetic-to-real image refinement applied to tilt series of cryogenic electron micrographs. It explores the possibility of improving the realism of synthesized micrographs using generative adversarial networks, which could help to improve the automatic segmentation of cellular structures based on deep learning methods. For image refinement, three image-to-image translation networks were used to transfer the appearance of real micrographs to synthetic micrographs while preserving their original content, including the location and shape of particles. The first model, called SimGAN, was unable to produce any meaningful refinement. Instead, the content of the synthetic micrographs was corrupted by the addition of extensive noise, making SimGAN unsuitable for the problem of this thesis. As a result, CycleGAN was introduced and its refinement of synthetic micrographs matches the appearance of real micrographs very well. However, structural changes in the position and shape of particles were observed after translation. To avoid this behavior, CUT was used as a third model on an exploratory basis but its performance was inferior to that of CycleGAN. In conclusion, CycleGAN proved to be the most promising image-to-image translation model for the images presented, although it does not solve the main problem of this thesis. In order to do so, further modifications, such as the addition of a structural constraint during translation, are required.}, language = {en} } @phdthesis{Tack2024, author = {Tack, Alexander}, title = {Machine Learning-based Assessment of Multiple Anatomical Structures in Medical Image Data for Diagnosis and Prediction of Knee Osteoarthritis}, doi = {10.14279/depositonce-19738}, year = {2024}, abstract = {Knee osteoarthritis (KOA) is a degenerative disease that leads to pain and loss of function. It is estimated to affect over 500 million humans world-wide and is one of the most common reasons for disability. KOA is usually diagnosed by radiologists or clinical experts by anamnesis, physical examination, and by assessing medical image data. The latter is typically acquired using X-Ray or magnetic resonance imaging. Since manual image reading is subjective, tedious and time-consuming, automated methods are required for a fast and objective decision support and for a better understanding of the pathogenesis of KOA. This thesis sets a foundation towards automated computation of image-based KOA biomarkers for holistic assessment of the knee. This involves the assessment of multiple knee bones and soft tissues. An assessment of particular structures requires localization of these tissues. In order to automate a faithful localization of anatomical structures, deep learning-based methods are investigated and utilized. Additionally, convolutional neural networks (CNNs) are used for classification of medical image data, i.e., for a direct determination of the disease status and to detect anatomical structures and landmarks. The automatically computed anatomical volumes, locations, and other measurements are finally compared to values acquired by clinical experts and evaluated for clustering of KOA groups, classification of KOA severity, prediction of KOA progression, and prediction of total knee replacement. In various experiments it is shown that CNN-based methods are suitable for accurate medical image segmentation, object detection, landmark detection, and direct classification of disease stages from the image data. Computed features related to the menisci are found to be most expressive in terms of clustering of KOA groups and predicting of future disease states, thus allowing diagnosis of current KOA conditions and prediction of future conditions. The conclusion of this thesis is that machine learning-based, fully automated processing of medical image data shows potential for diagnosis and prediction of KOA grades. Future studies could investigate additional features in order to achieve an assessment of the whole knee or validate the findings of this work in clinical studies.}, language = {en} } @article{EiflerGleixner2023, author = {Eifler, Leon and Gleixner, Ambros}, title = {A computational status update for exact rational mixed integer programming}, volume = {197}, journal = {Mathematical Programming}, doi = {10.1007/s10107-021-01749-5}, pages = {793 -- 812}, year = {2023}, abstract = {The last milestone achievement for the roundoff-error-free solution of general mixed integer programs over the rational numbers was a hybrid-precision branch-and-bound algorithm published by Cook, Koch, Steffy, and Wolter in 2013. We describe a substantial revision and extension of this framework that integrates symbolic presolving, features an exact repair step for solutions from primal heuristics, employs a faster rational LP solver based on LP iterative refinement, and is able to produce independently verifiable certificates of optimality. We study the significantly improved performance and give insights into the computational behavior of the new algorithmic components. On the MIPLIB 2017 benchmark set, we observe an average speedup of 10.7x over the original framework and 2.9 times as many instances solved within a time limit of two hours.}, language = {en} } @article{BesanconGarciaLegatetal.2023, author = {Besan{\c{c}}on, Mathieu and Garcia, Joaquim Dias and Legat, Beno{\^i}t and Sharma, Akshay}, title = {Flexible Differentiable Optimization via Model Transformations}, volume = {36}, journal = {INFORMS Journal on Computing}, number = {2}, doi = {10.1287/ijoc.2022.0283}, pages = {456 -- 478}, year = {2023}, abstract = {We introduce DiffOpt.jl, a Julia library to differentiate through the solution of optimization problems with respect to arbitrary parameters present in the objective and/or constraints. The library builds upon MathOptInterface, thus leveraging the rich ecosystem of solvers and composing well with modeling languages like JuMP. DiffOpt offers both forward and reverse differentiation modes, enabling multiple use cases from hyperparameter optimization to backpropagation and sensitivity analysis, bridging constrained optimization with end-to-end differentiable programming. DiffOpt is built on two known rules for differentiating quadratic programming and conic programming standard forms. However, thanks to its ability to differentiate through model transformations, the user is not limited to these forms and can differentiate with respect to the parameters of any model that can be reformulated into these standard forms. This notably includes programs mixing affine conic constraints and convex quadratic constraints or objective function.}, language = {en} } @article{BinkowskiKulligBetzetal.2024, author = {Binkowski, Felix and Kullig, Julius and Betz, Fridtjof and Zschiedrich, Lin and Walther, Andrea and Wiersig, Jan and Burger, Sven}, title = {Computing eigenfrequency sensitivities near exceptional points}, volume = {6}, journal = {Phys. Rev. Research}, arxiv = {http://arxiv.org/abs/2402.17648}, doi = {10.1103/PhysRevResearch.6.023148}, pages = {023148}, year = {2024}, language = {en} }